class_docstr: Class for masking of Niimg-like objects.
  
  MultiNiftiLabelsMasker is useful when data from non-overlapping volumes
  and from different subjects should be extracted (contrary to
  :class:`nilearn.maskers.NiftiLabelsMasker`).
  
  Parameters
  ----------
  labels_img : Niimg-like object
      See :ref:`extracting_data`.
      Region definitions, as one image of labels.
  
  labels : :obj:`list` of :obj:`str`, optional
      Full labels corresponding to the labels image. This is used
      to improve reporting quality if provided.
  
      .. warning::
          The labels must be consistent with the label
          values provided through `labels_img`.
  
  background_label : :obj:`int` or :obj:`float`, default=0
      Label used in labels_img to represent background.
      Warning: This value must be consistent with label values and
      image provided.
  
  mask_img : Niimg-like object, optional
      See :ref:`extracting_data`.
      Mask to apply to regions before extracting signals.
  %(smoothing_fwhm)s
  %(standardize_maskers)s
  %(standardize_confounds)s
  high_variance_confounds : :obj:`bool`, default=False
      If True, high variance confounds are computed on provided image with
      :func:`nilearn.image.high_variance_confounds` and default parameters
      and regressed out.
  %(detrend)s
  %(low_pass)s
  %(high_pass)s
  %(t_r)s
  dtype : {dtype, "auto"}
      Data type toward which the data should be converted. If "auto", the
      data will be converted to int32 if dtype is discrete and float32 if it
      is continuous.
  
  resampling_target : {"data", "labels", None}, default="data"
      Gives which image gives the final shape/size:
  
          - "data" means the atlas is resampled to the
            shape of the data if needed
          - "labels" means en mask_img and images provided to fit() are
            resampled to the shape and affine of maps_img
          - None means no resampling: if shapes and affines do not match, a
            ValueError is raised
  
  
  %(memory)s
  %(memory_level1)s
  %(n_jobs)s
  %(verbose0)s
  strategy : :obj:`str`, default='mean'
      The name of a valid function to reduce the region with.
      Must be one of: sum, mean, median, minimum, maximum, variance,
      standard_deviation.
  
  reports : :obj:`bool`, default=True
      If set to True, data is saved in order to produce a report.
  
  %(masker_kwargs)s
  
  Attributes
  ----------
  mask_img_ : :obj:`nibabel.nifti1.Nifti1Image`
      The mask of the data, or the computed one.
  
  labels_img_ : :obj:`nibabel.nifti1.Nifti1Image`
      The labels image.
  
  n_elements_ : :obj:`int`
      The number of discrete values in the mask.
      This is equivalent to the number of unique values in the mask image,
      ignoring the background value.
  
      .. versionadded:: 0.9.2
  
  See Also
  --------
  nilearn.maskers.NiftiMasker
  nilearn.maskers.NiftiLabelsMasker
code: def __init__(
      self,
      labels_img,
      labels=None,
      background_label=0,
      mask_img=None,
      smoothing_fwhm=None,
      standardize=False,
      standardize_confounds=True,
      high_variance_confounds=False,
      detrend=False,
      low_pass=None,
      high_pass=None,
      t_r=None,
      dtype=None,
      resampling_target="data",
      memory=Memory(location=None, verbose=0),
      memory_level=1,
      verbose=0,
      strategy="mean",
      reports=True,
      n_jobs=1,
      **kwargs,
  ):
      self.n_jobs = n_jobs
      super().__init__(
          labels_img,
          labels=labels,
          background_label=background_label,
          mask_img=mask_img,
          smoothing_fwhm=smoothing_fwhm,
          standardize=standardize,
          standardize_confounds=standardize_confounds,
          high_variance_confounds=high_variance_confounds,
          low_pass=low_pass,
          high_pass=high_pass,
          detrend=detrend,
          t_r=t_r,
          dtype=dtype,
          resampling_target=resampling_target,
          memory=memory,
          memory_level=memory_level,
          verbose=verbose,
          strategy=strategy,
          reports=reports,
          **kwargs,
      )
  
file_path: nilearn/nilearn/maskers/multi_nifti_labels_masker.py
imports: import itertools
  from joblib import Memory, Parallel, delayed
  from .._utils import fill_doc
  from .._utils.niimg_conversions import iter_check_niimg
  from .nifti_labels_masker import NiftiLabelsMasker
label_desc: Use image node_id 1 to create a new MultiNiftiLabelsMasker object from inherited base classes: NiftiLabelsMasker with example: obj = MultiNiftiLabelsMasker(labels_img, labels, background_label, mask_img, smoothing_fwhm, standardize, standardize_confounds, high_variance_confounds, detrend, low_pass, high_pass, t_r, dtype, resampling_target, memory, memory_level, verbose, strategy, reports, n_jobs)